Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios
(2022) In Proceedings of the National Academy of Sciences of the United States of America 119(52). p.2203200119-2203200119- Abstract
Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced... (More)
Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.
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- author
- Cano, Isabel Martínez ; Shevliakova, Elena ; Malyshev, Sergey ; John, Jasmin G. ; Yu, Yan ; Smith, Benjamin LU and Pacala, Stephen W.
- organization
- publishing date
- 2022-12-27
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- earth system model, forest recovery, tropical forest, wildfires
- in
- Proceedings of the National Academy of Sciences of the United States of America
- volume
- 119
- issue
- 52
- pages
- 2203200119 - 2203200119
- publisher
- National Academy of Sciences
- external identifiers
-
- pmid:36534807
- scopus:85144289822
- ISSN
- 1091-6490
- DOI
- 10.1073/pnas.2203200119
- language
- English
- LU publication?
- yes
- id
- 2724cb3e-e6ba-47bc-99e6-49d40249111b
- date added to LUP
- 2023-01-05 08:55:42
- date last changed
- 2024-06-13 22:47:06
@article{2724cb3e-e6ba-47bc-99e6-49d40249111b,
abstract = {{<p>Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.</p>}},
author = {{Cano, Isabel Martínez and Shevliakova, Elena and Malyshev, Sergey and John, Jasmin G. and Yu, Yan and Smith, Benjamin and Pacala, Stephen W.}},
issn = {{1091-6490}},
keywords = {{earth system model; forest recovery; tropical forest; wildfires}},
language = {{eng}},
month = {{12}},
number = {{52}},
pages = {{2203200119--2203200119}},
publisher = {{National Academy of Sciences}},
series = {{Proceedings of the National Academy of Sciences of the United States of America}},
title = {{Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios}},
url = {{http://dx.doi.org/10.1073/pnas.2203200119}},
doi = {{10.1073/pnas.2203200119}},
volume = {{119}},
year = {{2022}},
}